The robust and accurate retrieval of vegetation biophysical variables using radiative transfer models (RTM) is seriously
hampered by the ill-posedness of the inverse problem. With this research we further develop our previously published
(object-based) inversion approach [Atzberger (2004)]. The object-based RTM inversion takes advantage of the
geostatistical fact that the biophysical characteristics of nearby pixel are generally more similar than those at a larger
distance. A two-step inversion based on PROSPECT+SAIL generated look-up-tables is presented that can be easily
implemented and adapted to other radiative transfer models. The approach takes into account the spectral signatures of
neighboring pixel and optimizes a common value of the average leaf angle (ALA) for all pixel of a given image object,
such as an agricultural field. Using a large set of leaf area index (LAI) measurements (n = 58) acquired over six different
crops of the Barrax test site (Spain), we demonstrate that the proposed geostatistical regularization yields in most cases
more accurate and spatially consistent results compared to the traditional (pixel-based) inversion. Pros and cons of the
approach are discussed and possible future extensions presented.